{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Tutorial\n",
    "\n",
    "![trump](./images/trump.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /root/miniconda3/lib/python3.6/site-packages\n",
      "Requirement already satisfied: six in /root/miniconda3/lib/python3.6/site-packages (from nltk)\n",
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install nltk\n",
    "!pip install -U spacy\n",
    "!python -m spacy download en\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import re\n",
    "\n",
    "import spacy\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
    "\n",
    "import itertools\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>created_at</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>is_retweet</th>\n",
       "      <th>id_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>i think senator blumenthal should take a nice ...</td>\n",
       "      <td>2017-08-07 20:48:54</td>\n",
       "      <td>61446</td>\n",
       "      <td>false</td>\n",
       "      <td>8.946617e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>how much longer will the failing nytimes with ...</td>\n",
       "      <td>2017-08-07 20:39:46</td>\n",
       "      <td>42235</td>\n",
       "      <td>false</td>\n",
       "      <td>8.946594e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>the fake news media will not talk about the im...</td>\n",
       "      <td>2017-08-07 20:15:18</td>\n",
       "      <td>45050</td>\n",
       "      <td>false</td>\n",
       "      <td>8.946532e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>on #purpleheartday i thank all the brave men a...</td>\n",
       "      <td>2017-08-07 18:03:42</td>\n",
       "      <td>48472</td>\n",
       "      <td>false</td>\n",
       "      <td>8.946201e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>...conquests how brave he was and it was all a...</td>\n",
       "      <td>2017-08-07 12:01:20</td>\n",
       "      <td>59253</td>\n",
       "      <td>false</td>\n",
       "      <td>8.945289e+17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               source                                               text  \\\n",
       "0  Twitter for iPhone  i think senator blumenthal should take a nice ...   \n",
       "1  Twitter for iPhone  how much longer will the failing nytimes with ...   \n",
       "2  Twitter for iPhone  the fake news media will not talk about the im...   \n",
       "4  Twitter for iPhone  on #purpleheartday i thank all the brave men a...   \n",
       "5  Twitter for iPhone  ...conquests how brave he was and it was all a...   \n",
       "\n",
       "           created_at favorite_count is_retweet        id_str  \n",
       "0 2017-08-07 20:48:54          61446      false  8.946617e+17  \n",
       "1 2017-08-07 20:39:46          42235      false  8.946594e+17  \n",
       "2 2017-08-07 20:15:18          45050      false  8.946532e+17  \n",
       "4 2017-08-07 18:03:42          48472      false  8.946201e+17  \n",
       "5 2017-08-07 12:01:20          59253      false  8.945289e+17  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/trump.csv') # might need to change location if on Floydhub\n",
    "\n",
    "df = df[df.is_retweet=='false']\n",
    "df.text = df.text.str.lower()\n",
    "\n",
    "df = df[~df.text.str.startswith('rt')]\n",
    "df.text = df.text.str.replace(r'http[\\w:/\\.]+','') # remove urls\n",
    "df.text = df.text.str.replace(r'[^!\\'\"#$%&\\()*+,-./:;<=>?@_’`{|}~\\w\\s]',' ') #remove everything but characters and punctuation\n",
    "df.text = df.text.str.replace(r'\\s\\s+',' ') #replace multple white space with a single one\n",
    "df = df[[len(t)<180 for t in df.text.values]]\n",
    "df = df[[len(t)>50 for t in df.text.values]]\n",
    "\n",
    "df['created_at'] = pd.to_datetime(df['created_at'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source                    object\n",
       "text                      object\n",
       "created_at        datetime64[ns]\n",
       "favorite_count            object\n",
       "is_retweet                object\n",
       "id_str                   float64\n",
       "sentiment                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   23902\n",
       "unique                  23901\n",
       "top       2016-01-14 05:45:41\n",
       "freq                        2\n",
       "first     2009-05-04 18:54:25\n",
       "last      2017-08-07 20:48:54\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `apply` statement to use any custom function to be applied along the columns of a dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.5719\n",
      "0.8519\n"
     ]
    }
   ],
   "source": [
    "sia = SIA()\n",
    "print(sia.polarity_scores('I hate this movie it is aweful')['compound'])\n",
    "print(sia.polarity_scores('I love this movie it is awesome')['compound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8AADMw7ohubs/190fHZ7/WZLHklya5Pokdw+L3Z3k9cPz65Mc7+7nuvszSU4nuXqzGw4A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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188dc02898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['sentiment'] = df.text.apply(lambda x: sia.polarity_scores(x)['compound'])\n",
    "df[np.abs(df.sentiment)>1e-2].sentiment.hist(bins=100, figsize=(12,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you are putting in two conditions you cannot use the usual `and` clause that you would use in a python statement. Instead we have to use the bitwise `&`. It also seems that each conditional statement needs to be wrapped in brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f188c8906a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAa8De86fJQFgNSxjusUzkrx6cv/VSb5/Ca8BAABLU909/slVf57kXDamW/zH7r61qv6q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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188d5fdf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(np.abs(df.sentiment)>1e-2) & (df.created_at.dt.year>=2016)].sentiment.hist(bins=100, figsize=(12,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the most used words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('@realdonaldtrump', 5930),\n",
       " ('great', 2778),\n",
       " ('trump', 2738),\n",
       " ('&amp;', 2085),\n",
       " ('-', 1670),\n",
       " ('thank', 1390),\n",
       " ('donald', 1137),\n",
       " ('new', 1061),\n",
       " ('would', 1043),\n",
       " ('get', 1007),\n",
       " ('president', 994),\n",
       " ('obama', 983),\n",
       " ('like', 977),\n",
       " ('people', 967),\n",
       " ('make', 894),\n",
       " ('one', 822),\n",
       " ('america', 758),\n",
       " ('big', 708),\n",
       " ('run', 698),\n",
       " ('time', 692)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweeets = ' '.join(df.text.values)\n",
    "all_tweets = re.sub(r'[^a-z ]','', all_tweeets)\n",
    "words = all_tweeets.split()\n",
    "words = [word for word in words if word not in stopwords.words('english')]\n",
    "word_count = Counter(words)\n",
    "word_count.most_common(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweeting Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8zVTV+PbcoMa/qCp3vbWGTzYU8sfpwzj3xJR2OW5cZDi3TM7g3VvG86spg0iM\nabhtwS2NJYJ5OQWkxHX9dgauYDBpcCI79pd9e1+GL2hO76DLVDVFVcNVNU1Vn1HVgarap35XUFV9\nU1WznLKRqvpvr+Nkq+owVR2gqrfUVSG56b3V+cx4cimRESHMufk0xmckEBIi3DF1EDuLy3gte5fb\nIZoAcv9HG3lj+W5+eVYGV449we1wOlX9RPDV1v18tnkfZw8NrAHjmlLXVXThBt+5Ggi8e7SbQVV5\nbGEuM2etYFjvON6++XQGJn03fO2kwUmMTO/OPxbkUl5V42KkJlA88/k2nli0hctPTee2s4OjDrw+\n70Rw+dNfcbSqJih6BXnr0yOKAYndWORDQ0sHXRKoqK7hjtdX88BHG5k+PJWXf3oqPet1zRMR7pg6\nmL2Hy3lp6Q6XIjWB4p1Vefxxbg7TspL54/RhQXXmW19dIkjt3pW4yHDGBuCAcU2ZODiJpVv3c7TS\nN04wgyoJHCit5KpnlvHmit3cdnYGD186vNH+uqcNSOD0gT15YtEWSiuqG9zGmKZ8trmIO17/hjH9\nevDwjOHW4wxPIph7yxm8M/N0uoT5Tn/5zjJpcBKV1bUs2brP7VCAIEoCW4tKuOjxL1i18yAPXzqc\n284e1OQZ2R3nDGZ/aSXPfbGtk6I0gWT17oPc9H/LGZAYzVNXj/apG4TcFhcVTt+Ebm6H4YpT+sUT\nFRHKwg2+USUUFElgyZb9XPT4lxwur2bWz07lwhGN3qz8PSPS4zl7aBL/WryVQ2XtN4CXCXzb9pVy\n3XNfE98tgheuHxPQ4+KYlukS9l1XUR/oHxP4SeC17F1c/exXJERH8PbNpzO6b8vqIG+fMpgj5dU8\n+dmWDorQBJrCI+Vc/exXKPDi9WPo1cAY/Ca4TRycyO4DR9lS5H5X0YBNArW1yl8+3MCv31jNmH49\neOvm00nv2fJb0zNTY/nhyak898X2No3gaIJDdU0tv5i9kqIjFTx37SkNjutjzMTBnuFwFvnAjWMB\nmQSOVtYwc9YKnli0hcvGpPP8dW27HP/V2RlUVNfy+EK7GjDH9/cFm1m6tZj/vfDE7w2RYIy3tPgo\nMpKifWJo6YBLAqrKHa9/w4fr9vK784dy30XD2jxlXf/EaH48sjcvfbWD/EPNG8LXBJ/Fm4p4dGEu\nl4xK4+JRxx1d3RgmDUli2bZi13sfBlwSePmrnby3Jp//mjqYn57Rv936ZP/irAxUlUcW5LbL8Uxg\n2XuonNtqrgjHAAAajklEQVReXcWgpBjumT7M7XCMH5g4KJHKmlq+3OLuqKIBlQRy9hzmnrk5TBiU\nyE0TBrTrsdPio7h8TDqvZ+9iuw+N+2HcV9cOUF5Vw2NXjOywOYFNYBndtwfdIkJdbxcImCRQWlHN\nLbNXEBcZzt9+cjIhHXBTzszJAwkLFR6ev6ndj23811/nbWLZ9mLuu+hEBiZZQ7BpnoiwEE4fmMCi\njUWudhUNmCTwP++sZdu+Uv4+Y3ijMzS1VVJMV645rS/vfLOHjXuPdMhrGP+ycGMhTyzawoxT+jT7\n/hNj6kwakkTewaPkFpa4FkNAJIE3lu/mrRV5/GJyBqcNSOjQ17ppwgCiI8L427yNHfo6xvftOXiU\n219dxZDkGH5/QZbb4Rg/VNdV1M05Bvw+CeQWeqbTO7VfD35xVsePzhjfLYIbzujHR+sKmpxE2wSu\nqppabp29ksrqWh6/YqQNCWFaJSUukiHJMa52FfXrJFBeVcMts1YQGRHK32eM6LTBuW4Y34/4qHAe\n/NjaBoLVgx9tZPmOA/zpxyfZDWGmTc4cnMjX24spcamrqF8ngXvm5rBh7xH++pOTSY7rvFvzY7qG\nc9OZA1i8qYhl24qb3sEElAXrC/jX4q1ccWo6F5yc6nY4xs9NGpxEVY3yRa47o4r6bRKYu3oPs77a\nyc8n9Hdl0uarx/UlMaYLD3600ScGgTKdY/eBMm5/7RuyUmP5nx9kuh2OCQCjTognpkuYa11FmzPH\n8LMiUigia73KeojIPBHZ7PyM91p3l4jkishGEZnqVT5KRNY46x6RNtzFtXN/GXe9uYYR6d25Y+rg\n1h6mTSIjQrl18kCWbS9m8WbfGBfcdKzK6lpumbWSmlrlscutHcC0j/DQEMZnuNdVtDlXAs8D0+qV\n3QksUNUMYIHzHBHJBGYAWc4+j4tI3X/KE8DPgAznUf+YzVJZXcsts1cgAo/MGNHmISHaYsYp6fTu\nHslfP7argWBw/4cbWLXrIPdffFLQjoVvOsbEwYnkHypnY0Hndz1vzkTzi4H6Fd/TgRec5ReAC73K\nX1HVClXdBuQCY0QkBYhV1aXOBPMveu3TIn/5cAOrdx/i/otPok+Plo8K2p4iwkK47ewMVu8+xEfr\nClyNxXSsj9ft5enPt3HNuBM478QUt8MxAaZuAno3egm19jS6l6rmO8t7gbrZonsDu7y22+2U9XaW\n65c3SERuFJFsEckuKvruTZmfU8Azzj/itGG+8Y940Yje9E/sxt/mbaSm1q4GAtGu4jLueP0bTuwd\nx2/OH+p2OCYA9YrtytCUWBZu6Px2gTbXpThn9u367aeqT6rqaFUdnZjouZliz8Gj3PGGp0HurvN8\n5x8xLDSE26cMYlNBCf/+Zo/b4Zh25mkHWIECj10+MijnxDWdY9LgRJbvOMDh8s6dxbC1SaDAqeLB\n+VmXvvKAPl7bpTllec5y/fJmqRugq6q6lkd9sEHuvGEpDE2J5aH5m6iuqXU7HNOO7nt/Pd/sPsQD\nF5/cqkmJjGmuiYOTqK5VvujkjiatTQLvAtc4y9cA73iVzxCRLiLSD08D8DKn6uiwiIx1egVd7bVP\nkx6av4nsHQe470cn0s8HG+RCQoRbJw9kx/4ylmx1d1hY034+WJPP819u57rT+zJtWLLb4ZgANzK9\nOzFdwzq9XaA5XURnA0uAwSKyW0RuAP4MTBGRzcDZznNUdR3wGpADfAjMVNUa51A3A0/jaSzeAnzQ\nnABLKqp5fNEWLh3dh+nDfXeArslDkogMD+WjdXvdDsW0g4rqGn4zZw0np8Vx17m+U/1oAldYaAgT\nMhJZtKlzJ6APa2oDVb2skVVnNbL9vcC9DZRnAy2ebWNXcRmnJUb7/ABdXcNDmTg4kY/XFXDPBcM6\nZChr03kWbijkQFkVD106iIgwv72n0viZiYMTeW9NPuvzj5CZGtspr+nzn+4aVb+ZqGNqVjKFRypY\nucsGlvN3b63IIyG6C+MHduyotMZ4O9OFUUV9Pgn0jotkUK8Yt8NolklDkggLET62KiG/dqC0koUb\nC7lweCphLt6MaIJPUkxXhvWO5dNObBfw+U94fLcIt0NotrjIcE4bmMBH6/baHcR+bO6afKpqlItG\n+m4blAlcEwclsXznAQ4d7Zyuoj6fBPzN1KxebN9f5srt36Z9zFmxm8G9YshM6Zw6WWO8TRqSSE2t\n8nkndRW1JNDOpmT2QgQ+WmvDSPij7ftKWbHzIBeN7E0bxjg0ptWG94knLjK809oFLAm0s6SYroxK\nj7euon5qzso8RGD6cJsnwLgjNESYMCiRTzcVUdsJQ9FYEugAU7OSyck/zK7iMrdDMS2gqry9Ko/T\nBvQkJS7S7XBMEJs4KJGiIxXk5B/u8NeyJNABpmZ57i61qwH/smLnAXbsL+OiEWlNb2xMB6rrKjpn\nZV6HD0VjSaADpPeMYmhKrCUBP/PWijy6hofYEBHGdQnRXTgjI4FnPt/GmPsWcNdba/gid1+HJIQm\n7xg2rTM1qxd/X7CZoiMVJMZ0cTsc04SK6hrmrs5nalYy0V3s38K476mrR/PppiLeW53PO6vymL1s\nJz27RTB1WDI/ODGFU/v3JLQdRiawT3sHmZqVzMPzNzMvp4DLT013OxzThIUbijh0tIqLRti9AcY3\ndA0PZWpWMlOzkimvqmHRxkLmrs5nzoo8Zn21k4ToCKYNS+b8E1MZ069HqxOCJYEOMiQ5hhN6RvHR\nur2WBPzAnJW7bZgI47O6hocybVgK04alcLTSSQhr8nlzeR4vLd1JQnQXzjsxmfNPTGF035YlBEsC\nHUREmJqVzHNfbONweRWxXcPdDsk04mBZJZ9sKOTqcX1tmAjj8yIjQjn3xBTOPTGFsspqFm4o4r01\ne3gtexcvLtlBUkyXFk2BakmgA03N6sWTi7eycEOhTw+DHezmrnaGibCqIONnoiLCOP+kFM4/KYXS\nimoWbizkvdX5zF62s9nHsCTQgUb0iScxpgsfrdtrScCHzVmZx6Be0WR10tC9xnSEbl3C+MFJqfzg\npFRKK6qJPmZA/4bZtW8HCgkRzsnsxcINRZRX1TS9g+l02/eVsnzHAS4akWbDRJiA0a0FPdwsCXSw\nqVnJHK2q4bNOnjfUNE/dMBEXjrBhIkxwsiTQwcb270ls1zC7ccwH1Q0TMa6/DRNhglerk4CIDBaR\nVV6PwyJym4j8XkTyvMrP89rnLhHJFZGNIjK1fX4F3xYRFsJZQ3sxf31Bh9/+bVrmu2EirL3GBK9W\nJwFV3aiqw1V1ODAKKAPmOKsfqlunqu8DiEgmMAPIAqYBj4uI788Z2Q6mZvXiYFkVy7YVux2K8VI3\nTMS5LehOZ0ygaa/qoLOALaq64zjbTAdeUdUKVd0G5AJj2un1fdqEQYl0DQ+xKiEfUjdMxDmZNkyE\nCW7tlQRmALO9nt8qIqtF5FkRiXfKegO7vLbZ7ZQFvKiIMCZkJPLRuoJOGR/cNO3bYSJsCkkT5Nqc\nBEQkArgAeN0pegLoDwwH8oG/tuKYN4pItohkFxV13oTLHWlqVjJ7D5ezOu+Q26EYvhsm4gwbJsIE\nufa4EjgXWKGqBQCqWqCqNapaCzzFd1U+eUAfr/3SnLJjqOqTqjpaVUcnJia2Q4juO2toEmEhYlVC\nPqBumIgLTk61YSJM0GuP/4DL8KoKEhHvVraLgLXO8rvADBHpIiL9gAxgWTu8vl/oHhXB2P49+Wjt\nXlStSshNdcNE/MiqgoxpWxIQkW7AFOAtr+L7RWSNiKwGJgG/AlDVdcBrQA7wITBTVYPqNtqpWb3Y\nuq+U3MISt0MJajZMhDHfaVMSUNVSVe2pqoe8yq5S1RNV9SRVvUBV873W3auqA1R1sKp+0JbX9kfn\n2LSTrtux34aJMMabVYh2ol6xXRmR3p0PLQm4xoaJMOb7LAl0sqlZyazNO8zuA2VuhxJ0VJU5K22Y\nCGO8WRLoZFOdKqGP1xW4HEnwWbHzoA0TYUw9lgQ6Wb+EbgzuFWNVQi6Ys3K3DRNhTD2WBFwwNasX\n2duL2VdS4XYoQaOyutaGiTCmAZYEXDB1WDK1CvNzrEqosyzcWMjBMhsmwpj6LAm4IDMllrT4SOsq\n2onmrMizYSKMaYAlAReICFOzkvkidz9HyqvcDifgHSqrsmEijGmE/Ue4ZNqwZCpralm4MTAGyPNl\nc9fsobKm1oaJMKYBlgRcMjI9noToCKsS6gRzVuSRkWTDRBjTEEsCLgkNEaZk9mLRhkLKq4JqCKVO\ntWN/Kdk7DnDRyN42TIQxDbAk4KKpWcmUVtbwRe4+t0MJWC8t3eEZJmK4VQUZ0xBLAi46bUACMV3C\nrEqog2wpKuH5L7dz8cg0UrvbMBHGNMSSgIsiwkKYNCSJ+esLqa6pdTucgKKq3PPvHLqGhfLraUPc\nDscYn2VJwGXThiVTXFrJ19sPuB1KQFmwvpBPNxVx25RBJMZ0cTscY3yWJQGXnTkokYiwEN79psGZ\nNk0rlFfVcM/cHDKSorl63Aluh2OMT7Mk4LJuXcK4dHQfZi/bZcNItJNnPt/GzuIy7v5hFuF2c5gx\nx2X/IT7gt+cP5cTecfzqtVVs31fqdjh+bc/Bozz6SS7TspIZn2FDRBjTFEsCPqBreCiPXzGS0BDh\nppeWc7TS7htorT99sIFaVX57/lC3QzHGL7R1ovntzqTyq0Qk2ynrISLzRGSz8zPea/u7RCRXRDaK\nyNS2Bh9I+vSI4uFLh7Ox4Ai/mbMGVXU7JL+zdOt+/v3NHm46cwB9ekS5HY4xfqE9rgQmqepwVR3t\nPL8TWKCqGcAC5zkikgnMALKAacDjIhLaDq8fMCYOTuK2swYxZ2UeLy3d4XY4fqW6ppbfv7uO3t0j\n+Y+JA9wOxxi/0RHVQdOBF5zlF4ALvcpfUdUKVd0G5AJjOuD1/dqtkwcyaXAi98zNYcVO6zbaXLOW\n7WTD3iP87vyhdA23cwtjmqutSUCB+SKyXERudMp6qWq+s7wX6OUs9wZ2ee272yk7hojcKCLZIpJd\nVBRco2yGhAgPXzqClLhIbn5phc0+1gzFpZX89eNNnD6wJ9OGJbsdjjF+pa1JYLyqDgfOBWaKyATv\nleqp2G5x5baqPqmqo1V1dGJiYhtD9D9xUeE8ceVIDpRVcuuslXY3cRMe/HgjJRXV3P3DLBskzpgW\nalMSUNU852chMAdP9U6BiKQAOD8Lnc3zgD5eu6c5ZaYBWalx3HvRiSzZup8HP97kdjg+a23eIWYv\n28k14/oyqFeM2+EY43danQREpJuIxNQtA+cAa4F3gWucza4B3nGW3wVmiEgXEekHZADLWvv6weDi\nUWlcfmo6//x0Cx+utUHm6lNV7n53HT27RXDblAy3wzHGL4W1Yd9ewBzn8jsMmKWqH4rI18BrInID\nsAP4CYCqrhOR14AcoBqYqarWIb4Jd/8wk3V5h7jj9W8Y1Cua/onRbofkM95elcfyHQe4/8cnEds1\n3O1wjPFL4uv90UePHq3Z2dluh+GqvINH+eE/PichOoK3Z55OVERbcndgKKmoZvKDi0iJ68qcm08n\nJMTaAozxJiLLvbruN8ruGPYDvbtH8siMEeQWlnDnm3YjGcA/PtlM4ZEKfn9BliUAY9rAkoCfGJ+R\nwH+eM5h3v9nD819udzscV20tKuHZz7dxyag0RqTHN72DMaZRlgT8yH+cOYCzh/bi3vfWk7292O1w\nXKGq3DPXJosxpr1YEvAjISHCX39yMmnxkdz88goKj5S7HVKn+2RDIYs2FvHLszNsshhj2oElAT8T\nFxnOP68axeHyKm6ZtZKqILqRrG6ymIFJ0VxzWl+3wzEmIFgS8ENDkmP5849OYtm2Yu7/cIPb4XSa\nZz7fxo79Zdz9w0ybLMaYdmJ9Df3UhSN6s3LnAZ76bBtp8VEBf2acf8gzWczUrF6ckRF8Q4kY01Es\nCfix356fSf6hcu5+dx3VtcoN4/u5HVKHUFXufW89tar87vxMt8MxJqDYNbUfiwgL4bErRnLusGT+\nODeHpxZvdTukdldWWc2vXl3F3NX53DxxoE0WY0w7syTg58JDQ3jkshGcf1IK976/nicWbXE7pHaz\ntaiECx/7gne+2cMd5wzi1skD3Q7JmIBj1UEBIDw0hL9fOpxQEf7y4QZqamu5ZbJ/D6j24dp87nh9\nNeGhwovXj7F2AGM6iCWBABEWGsJDlw4nLER48ONNVNcqt509yO2wWqy6ppb7P9rIk4u3cnKf7jx+\nxUh6d490OyxjApYlgQASGiI8cMnJntnJ5m+mtlb51ZRBfjPRSuGRcm6ZtZJl24q5cmw6//ODTLqE\n2VSRxnQkSwIBJjREuP/HJxEWIjzySS7Vtcp/TR3s84ng6+3FzHx5BYfLq/jbT07mRyPT3A7JmKBg\nSSAAhYQI9110IiEhwuOLtlBTq9x57hCfTASqyjOfb+NPH2ygT3wkL1w/hqEpsW6HZUzQsCQQoEJC\nhHsvHEZYiPCvxVuprlV+d/5Qn0oEJRXV/Pcbq3lvTT7nZPbiwZ+cbJPDGNPJLAkEMBHhDxdkERoi\nPPP5Nmpqlbt/mOkTiWBzwRFuemk52/aVcue5Q/j5hP4+EZcxwcaSQIATEf7fDzIJFeHpz7dRXVvL\nPRcMc3Uiln9/s4f/fnM1URGhvPzTsYwb0NO1WIwJdm2ZaL6PiCwUkRwRWSciv3TKfy8ieSKyynmc\n57XPXSKSKyIbRWRqe/wCpmkiwm/PH8rPz+zPS0t38tu311Jb2/mzk1VW1/KHf6/j1tkrGZoSy9xb\nz7AEYIzL2nIlUA38p6quEJEYYLmIzHPWPaSqD3pvLCKZwAwgC0gF5ovIIJtsvnOICHdOG0JYiPDY\nwi3U1Nby5x+d1KFXBAdKK1mx84DnseMg3+w+SFllDded3pffnDfURgI1xge0Ogmoaj6Q7ywfEZH1\nQO/j7DIdeEVVK4BtIpILjAGWtDYG0zIiwh3nDCY0JIRHFmxm494jZKbGkd4jivQeUZzQM4o+PaKI\ni2x542xNrbK58Agrdhxk+Y4DrNx5gK37SgFPt9XMlFguGZXGWUN7MWGQ3f1rjK9olzYBEekLjAC+\nAk4HbhWRq4FsPFcLB/AkiKVeu+2mkaQhIjcCNwKkp6e3R4jGISLcPmUQ8VHhvLNqDx+v28v+0srv\nbRMXGf5tQkjvEcUJzs/0nlGkxEUSGiIcOlrFyp0HWLHzICt2HGDVroOUVFQD0LNbBCPS47l4dBqj\n0uM5Ka07kRF205cxvkhU21Y3LCLRwKfAvar6loj0AvYBCvwRSFHV60XkUWCpqr7k7PcM8IGqvnG8\n448ePVqzs7PbFKM5viPlVewqPsrO4jJ2Fpc6P4+yc38puw8cpdqr/SA8VOjZrQt7D3umtgwRzyQ3\nI0/ozsj0eEadEE96jyjr6WOMy0RkuaqObmq7Nl0JiEg48Cbwsqq+BaCqBV7rnwLmOk/zgD5eu6c5\nZcZlMV3DyUwNJzP12Ju0amqV/ENH2bm/zEkOZew9VE7/xG6MTI/npD7die5incyM8Vet/u8Vz6ne\nM8B6Vf2bV3mK014AcBGw1ll+F5glIn/D0zCcASxr7eubzhEaIqTFR5EWH8VpbgdjjGl3bTmFOx24\nClgjIqucst8Al4nIcDzVQduBnwOo6joReQ3IwdOzaKb1DDLGGHe1pXfQ50BDFb/vH2efe4F7W/ua\nxhhj2pd11DbGmCBmScAYY4KYJQFjjAlilgSMMSaIWRIwxpggZknAGGOCWJuHjehoInIE2Oh2HD4s\nAc8wHaZx9h41zd6jpvnbe3SCqjY5WqM/3O+/sTnjXwQrEcm29+f47D1qmr1HTQvU98iqg4wxJohZ\nEjDGmCDmD0ngSbcD8HH2/jTN3qOm2XvUtIB8j3y+YdgYY0zH8YcrAWOMMR3EkoAxxgQxn00CIjJN\nRDaKSK6I3Ol2PL5IRLaLyBoRWSUiNgcnICLPikihiKz1KushIvNEZLPzM97NGN3WyHv0exHJcz5L\nq0TkPDdjdJOI9BGRhSKSIyLrROSXTnlAfo58MgmISCjwGHAukIlnoppMd6PyWZNUdXgg9l9upeeB\nafXK7gQWqGoGsMB5Hsye59j3COAh57M0XFUbnRckCFQD/6mqmcBYYKbz/ROQnyOfTALAGCBXVbeq\naiXwCjDd5ZiMH1DVxUBxveLpwAvO8gvAhZ0alI9p5D0yDlXNV9UVzvIRYD3QmwD9HPlqEugN7PJ6\nvtspM9+nwHwRWS4iN7odjA/r5TXv9V6gl5vB+LBbRWS1U10UEFUdbSUifYERwFcE6OfIV5OAaZ7x\nqjocT7XZTBGZ4HZAvk49faKtX/SxngD6A8OBfOCv7objPhGJBt4EblPVw97rAulz5KtJIA/o4/U8\nzSkzXlQ1z/lZCMzBU41mjlUgIikAzs9Cl+PxOapaoKo1qloLPEWQf5ZEJBxPAnhZVd9yigPyc+Sr\nSeBrIENE+olIBDADeNflmHyKiHQTkZi6ZeAcYO3x9wpa7wLXOMvXAO+4GItPqvtyc1xEEH+WRESA\nZ4D1qvo3r1UB+Tny2TuGnS5qDwOhwLOqeq/LIfkUEemP5+wfPKPBzrL3CERkNjARz7C/BcDdwNvA\na0A6sAP4iaoGbcNoI+/RRDxVQQpsB37uVf8dVERkPPAZsAaodYp/g6ddIOA+Rz6bBIwxxnQ8X60O\nMsYY0wksCRhjTBCzJGCMMUHMkoAxxgQxSwLGGBPELAkY40VESuo9v1ZEHnUrHmM6miUBYzqBiIS5\nHYMxDbEkYEwziUhfEfnEGWRtgYikO+XPi8jFXtuVOD8nishnIvIukONS2MYcl52dGPN9kSKyyut5\nD74bsuQfwAuq+oKIXA88QtPDCY8EhqnqtvYP1Zi2syRgzPcddUZmBTxtAkDdhD3jgB85y/8H3N+M\n4y2zBGB8mVUHGdN21Tj/SyISAkR4rSt1JSJjmsmSgDHN9yWeEW0BrsAzyBh4Blwb5SxfAIR3bljG\ntJ4lAWOa71bgOhFZDVwF/NIpfwo4U0S+wVNlZGf/xm/YKKLGGBPE7ErAGGOCmCUBY4wJYpYEjDEm\niFkSMMaYIGZJwBhjgpglAWOMCWKWBIwxJoj9f+4xmSKwnvWkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4fd26b51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df.created_at.dt.hour).size().plot()\n",
    "plt.title('Number of Tweets at a Given Hour')\n",
    "plt.xlabel('Hour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>tweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2009-05-04</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-05-08</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-05-08</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-05-12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              day  hour  tweet_count\n",
       "count  2009-05-04    18            1\n",
       "1      2009-05-05     1            1\n",
       "2      2009-05-08    13            1\n",
       "3      2009-05-08    20            1\n",
       "4      2009-05-12    14            1"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_perday_perhour = df.groupby([df.created_at.dt.date, df.created_at.dt.hour]).size()\n",
    "tweets_perday_perhour.index.names = ['day', 'hour']\n",
    "tweets_perday_perhour = tweets_perday_perhour.reset_index()\n",
    "tweets_perday_perhour.rename(columns={tweets_perday_perhour.columns[-1]: \"tweet_count\" }, inplace=True)\n",
    "tweets_perday_perhour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fd25e2940>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ucTBrYjKeTVyMGVRPET+b2WPM0EI38Vc7bJjHeG1GliV+Y7wVkom/s9uFo8Np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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4fd25ea978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweet_group = tweets_perday_perhour.groupby('hour').agg({'tweet_count':{'Sum':'sum', 'Mean': 'mean', 'stddev':'std'}})\n",
    "tweet_group['tweet_count']['Mean'].plot()\n",
    "# tweet_group['tweet_count']['stddev'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2sZDtfdrmbxLR98LYyObwujMBHDDuczC8rgkiegMR3UpEt87MzPRpiYIgnK7U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V8Gg1EaLqUonVtTWspIIX75oY34uiLy5OOf3rEUD39iU9mvlUu5BjLtKdlQa0ipmeDZ2E2cej7U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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1888ed2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.Grouper(key='created_at', freq='7D')).size().plot(figsize=(12,5))\n",
    "plt.title('Number of Tweets per day')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of Tweets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apple ORG\n",
      "U.K. GPE\n",
      "$1 billion MONEY\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')\n",
    "\n",
    "for ent in doc.ents:\n",
    "    print(ent.text, ent.label_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entity extraction\n",
    "\n",
    "Unfortunately case matters when it comes to entity recognition so we will reimport the data below. We will also focus only on the tweets from 2016 onwards which I presume are the tweets dealing with the 2016 election. You know, the one that trump lost but won?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4651, 6)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/trump.csv') # might need to change location if on Floydhub\n",
    "\n",
    "df = df[df.is_retweet=='false']\n",
    "df = df[~df.text.str.startswith('RT ')]\n",
    "\n",
    "df = df[[len(t)<180 for t in df.text.values]]\n",
    "df = df[[len(t)>50 for t in df.text.values]]\n",
    "\n",
    "df['created_at'] = pd.to_datetime(df['created_at'])\n",
    "df = df[df.created_at.dt.year>=2016]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = [nlp(tweet) for tweet in df.text.values]\n",
    "entities = [doc.ents for doc in docs]\n",
    "entities = list(itertools.chain.from_iterable(entities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hillary', 199),\n",
       " ('today', 174),\n",
       " ('hillary clinton', 165),\n",
       " ('u.s.', 122),\n",
       " ('trump2016', 122),\n",
       " ('tonight', 112),\n",
       " ('america', 102),\n",
       " ('american', 100),\n",
       " ('tomorrow', 95),\n",
       " ('obama', 92),\n",
       " ('ted cruz', 89),\n",
       " ('trump', 79),\n",
       " ('clinton', 77),\n",
       " ('obamacare', 76),\n",
       " ('ohio', 74)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entities_names = [ent.text.lower() for ent in entities]\n",
    "Counter(entities_names).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ORG', 2416),\n",
       " ('PERSON', 2324),\n",
       " ('GPE', 2247),\n",
       " ('DATE', 843),\n",
       " ('MONEY', 713),\n",
       " ('NORP', 623),\n",
       " ('CARDINAL', 596),\n",
       " ('TIME', 456),\n",
       " ('ORDINAL', 95),\n",
       " ('FAC', 89),\n",
       " ('PERCENT', 78),\n",
       " ('WORK_OF_ART', 71),\n",
       " ('PRODUCT', 61),\n",
       " ('LOC', 60),\n",
       " ('EVENT', 46),\n",
       " ('LAW', 26),\n",
       " ('QUANTITY', 8),\n",
       " ('LANGUAGE', 3)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entities_types = [ent.label_ for ent in entities]\n",
    "Counter(entities_types).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hillary', 199),\n",
       " ('hillary clinton', 165),\n",
       " ('ted cruz', 89),\n",
       " ('obama', 81),\n",
       " ('clinton', 69),\n",
       " ('cruz', 66),\n",
       " ('crooked hillary', 56),\n",
       " ('trump2016', 56),\n",
       " ('donald trump', 44),\n",
       " ('bernie sanders', 34),\n",
       " ('trump', 29),\n",
       " ('vote', 28),\n",
       " ('marco rubio', 27),\n",
       " ('bernie', 18),\n",
       " (\"hillary clinton's\", 16)]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = [ent.text.lower() for ent in entities if ent.label_=='PERSON']\n",
    "Counter(people).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('u.s.', 122),\n",
       " ('america', 102),\n",
       " ('ohio', 74),\n",
       " ('florida', 72),\n",
       " ('russia', 66),\n",
       " ('iowa', 59),\n",
       " ('maga', 47),\n",
       " ('new hampshire', 46),\n",
       " ('pennsylvania', 42),\n",
       " ('melania', 37),\n",
       " ('china', 35),\n",
       " ('indiana', 34),\n",
       " ('south carolina', 33),\n",
       " ('mexico', 32),\n",
       " ('new york', 30)]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo = [ent.text.lower() for ent in entities if ent.label_=='GPE']\n",
    "Counter(geo).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('obamacare', 59),\n",
       " ('@nytimes', 46),\n",
       " ('cnn', 35),\n",
       " ('fbi', 32),\n",
       " ('@realdonaldtrump', 29),\n",
       " ('senate', 27),\n",
       " ('gop', 27),\n",
       " ('trump', 27),\n",
       " ('dnc', 25),\n",
       " ('@whitehouse', 25),\n",
       " ('the republican party', 20),\n",
       " ('congress', 18),\n",
       " ('the white house', 18),\n",
       " ('rubio', 17),\n",
       " ('state', 16)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org = [ent.text.lower() for ent in entities if ent.label_=='ORG']\n",
    "Counter(org).most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.text = df.text.str.lower()\n",
    "df.text = df.text.str.replace(r'http[\\w:/\\.]+','') # remove urls\n",
    "df.text = df.text.str.replace(r'[^!\\'\"#$%&\\()*+,-./:;<=>?@_’`{|}~\\w\\s]',' ') #remove everything but characters and punctuation\n",
    "df.text = df.text.str.replace(r'\\s\\s+',' ') #replace multple white space with a single one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=2000, stop_words='english')\n",
    "tf = tf_vectorizer.fit_transform(df.text.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reference: http://scikit-learn.org/stable/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py\n",
    "def print_top_words(model, feature_names, n_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        message = \"Topic #%d: \" % topic_idx\n",
    "        message += \" \".join([feature_names[i]\n",
    "                             for i in topic.argsort()[:-n_top_words - 1:-1]])\n",
    "        print(message)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/lib/python3.6/site-packages/sklearn/decomposition/online_lda.py:532: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topics in LDA model:\n",
      "Topic #0: thank great trump2016 makeamericagreatagain today realdonaldtrump tonight amp support enjoy\n",
      "Topic #1: hillary clinton trump crooked media president obama campaign amp want\n",
      "Topic #2: america great make new going people realdonaldtrump americafirst love states\n",
      "Topic #3: cruz join just said ted iowa amp republican maga rubio\n",
      "Topic #4: amp cnn jobs new people time great let poll trump\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lda = LatentDirichletAllocation(n_components=5)\n",
    "lda.fit(tf)\n",
    "\n",
    "print(\"\\nTopics in LDA model:\")\n",
    "tf_feature_names = tf_vectorizer.get_feature_names()\n",
    "print_top_words(lda, tf_feature_names, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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